






"""simple docstring"""





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Any		=					["""image_processor""", """tokenizer"""]
    SCREAMING_SNAKE_CASE		:	List[str]		=					"""LayoutLMv3ImageProcessor"""
    SCREAMING_SNAKE_CASE		:	Dict		=					("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")






    def __init__(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Tuple=None			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=None			,		**__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'			,		__SCREAMING_SNAKE_CASE			,		)
        lowerCamelCase_       	=		kwargs.pop('feature_extractor'   )

      lowerCamelCase_       	=		image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.'   )
      if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.'   )

      super().__init__(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def __call__(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		__SCREAMING_SNAKE_CASE	:				Union[List[List[int]], List[List[List[int]]]] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[List[int], List[List[int]]]] = None			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				Union[bool, str, PaddingStrategy] = False			,		__SCREAMING_SNAKE_CASE	:				Union[bool, str, TruncationStrategy] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		__SCREAMING_SNAKE_CASE	:				int = 0			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[bool] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[bool] = None			,		__SCREAMING_SNAKE_CASE	:				bool = False			,		__SCREAMING_SNAKE_CASE	:				bool = False			,		__SCREAMING_SNAKE_CASE	:				bool = False			,		__SCREAMING_SNAKE_CASE	:				bool = False			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, TensorType]] = None			,		**__SCREAMING_SNAKE_CASE	:				List[Any]			,		)	->						BatchEncoding:
      # verify input
      if self.image_processor.apply_ocr and (boxes is not None):
        raise ValueError(
            'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'   )

      if self.image_processor.apply_ocr and (word_labels is not None):
        raise ValueError(
            'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'   )

      # first, apply the image processor
      lowerCamelCase_       	=		self.image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors=__SCREAMING_SNAKE_CASE   )

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
        if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          lowerCamelCase_       	=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
        lowerCamelCase_       	=		features['words']

      lowerCamelCase_       	=		self.tokenizer(
          text=text if text is not None else features['words']			,		text_pair=text_pair if text_pair is not None else None			,		boxes=boxes if boxes is not None else features['boxes']			,		word_labels=__SCREAMING_SNAKE_CASE			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		padding=__SCREAMING_SNAKE_CASE			,		truncation=__SCREAMING_SNAKE_CASE			,		max_length=__SCREAMING_SNAKE_CASE			,		stride=__SCREAMING_SNAKE_CASE			,		pad_to_multiple_of=__SCREAMING_SNAKE_CASE			,		return_token_type_ids=__SCREAMING_SNAKE_CASE			,		return_attention_mask=__SCREAMING_SNAKE_CASE			,		return_overflowing_tokens=__SCREAMING_SNAKE_CASE			,		return_special_tokens_mask=__SCREAMING_SNAKE_CASE			,		return_offsets_mapping=__SCREAMING_SNAKE_CASE			,		return_length=__SCREAMING_SNAKE_CASE			,		verbose=__SCREAMING_SNAKE_CASE			,		return_tensors=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE			,		)

      # add pixel values
      lowerCamelCase_       	=		features.pop('pixel_values'   )
      if return_overflowing_tokens is True:
        lowerCamelCase_       	=		self.get_overflowing_images(__SCREAMING_SNAKE_CASE			,		encoded_inputs['overflow_to_sample_mapping']   )
      lowerCamelCase_       	=		images

      return encoded_inputs






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Optional[Any]:
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      lowerCamelCase_       	=		[]
      for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]   )

      if len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
        raise ValueError(
            'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
            F''' {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'''   )

      return images_with_overflow






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		*__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		**__SCREAMING_SNAKE_CASE	:				str   )	->						Tuple:
      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict			,		*__SCREAMING_SNAKE_CASE	:				Dict			,		**__SCREAMING_SNAKE_CASE	:				Dict   )	->						List[Any]:
      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    @property
    def   UpperCamelCase			(			self	:				int   )	->						Optional[int]:
      return ["input_ids", "bbox", "attention_mask", "pixel_values"]






    @property
    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,		__SCREAMING_SNAKE_CASE			,		)
      return self.image_processor_class






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,		__SCREAMING_SNAKE_CASE			,		)
      return self.image_processor












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						list[list[int | float]]					) ->							int:
  lowerCamelCase_       	=		len(_lowerCamelCase					)
  lowerCamelCase_       	=		len(matrix[0]					)
  lowerCamelCase_       	=		min(_lowerCamelCase						,							_lowerCamelCase					)

  for row in range(_lowerCamelCase					):
    # Check if diagonal element is not zero
    if matrix[row][row] != 0:
      # Eliminate all the elements below the diagonal
      for col in range(row + 1						,							_lowerCamelCase					):
        lowerCamelCase_       	=		matrix[col][row] / matrix[row][row]
        for i in range(_lowerCamelCase						,							_lowerCamelCase					):
          matrix[col][i] -= multiplier * matrix[row][i]
    else:
      # Find a non-zero diagonal element to swap rows
      lowerCamelCase_       	=		True
      for i in range(row + 1						,							_lowerCamelCase					):
        if matrix[i][row] != 0:
          lowerCamelCase_	,      lowerCamelCase_       	=		matrix[i], matrix[row]
          lowerCamelCase_       	=		False
          break
      if reduce:
        rank -= 1
        for i in range(_lowerCamelCase					):
          lowerCamelCase_       	=		matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
      row -= 1

  return rank


if __name__ == "__main__":
  import doctest

  doctest.testmod()




